Method for electrical energy meter correction in customer-distribution grid

ABSTRACT

A device, method, and computer-readable medium for correcting at least one error in readings of electricity meters, the method including receiving first readings of regular meters measuring electric energy delivered in each of a group of cables fed from a same distribution node in an electric grid during a period of time, receiving second readings of check meters measuring electric energy delivered in each of combinations of cables in the group of cables during the period of time, the combinations of cables being formed based on a redundant matrix in a generator matrix of a linear systematic block code, and correcting, in response to determining that at least one error been detected, the at least one error in the first readings of the regular meters and the second readings of the check meters.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent the work is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

A smart grid is a modernized electric grid that uses variouscommunication technologies to manage the electric grid in an automatedfashion to improve the efficiency and reliability of the production anddistribution of electricity. Smart grids provide a completelycontrollable and monitored network, where two-way communications betweendifferent elements of the grid and the control center are possible.

Electricity meters are used to monitor the electricity usage ofdifferent customers, both residential and industrial, and to providebilling information to customers as well as operators of the smartgrids. Wrong readings of electricity meters can result from tampering orfrom malfunctioning of an electricity meter. These wrong readings ofelectricity meters can result in a huge financial loss to the electricutility companies. For example, customers can bring forth complaintsagainst the electric utility companies and demand them to pay largepenalties for the wrong readings of electricity meters. Thus, methodsfor detecting and correcting wrong readings of electricity meters areimportant for electric utility companies.

SUMMARY

Aspects of the disclosure provide a method, device, andcomputer-readable medium for correcting at least one error in readingsof electricity meters. The method includes receiving first readings ofregular meters measuring electric energy delivered in each of a group ofcables fed from a same distribution node in an electric grid during aperiod of time, receiving second readings of check meters measuringelectric energy delivered in each of combinations of cables in the groupof cables during the period of time, the combinations of cables beingformed based on a redundant matrix in a generator matrix of a linearsystematic block code, and correcting, in response to determining thatat least one error has been detected, the at least one error in thefirst readings and/or the second readings based on a syndrome vector ofa reading vector that includes the first readings and the secondreadings, the syndrome vector of the reading vector being computed usinga decoding matrix that is obtained by modifying a parity-check matrix ofthe linear systematic block code.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of this disclosure that are proposed as exampleswill be described in detail with reference to the following figures,wherein like numerals reference like elements, and wherein:

FIG. 1 shows an example of linear (K, N) code with N=4 and K=7;

FIG. 2 shows an electric energy transmission system according to someembodiments of the disclosure;

FIG. 3 shows an exemplary configuration of an electric energytransmission system for measuring electric energy delivered by differentgroups of cables according to some embodiments of the disclosure;

FIG. 4 shows an electricity metering system using a coding schemeaccording to some embodiments of the disclosure;

FIG. 5 shows a process for detecting and correcting errors in readingsof electricity meters measuring electric energy delivered in a group ofcables fed from a same distribution node in an electric grid accordingto an embodiment of the disclosure;

FIG. 6 shows an exemplary computer for implementing various aspects ofthe disclosure according to some embodiments of the disclosure;

FIG. 7 shows an exemplary data processing system for implementingvarious aspects of the disclosure according to some embodiments of thedisclosure; and

FIG. 8 shows an exemplary implementation of a CPU according to anembodiment of the disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

One method for detecting and correcting errors in readings ofelectricity meters is to use an additional electricity meter on a cablefor electric energy transmission. This method is used by someelectricity providers on the feeders directed to some high-loadcustomers, e.g., industrial customers. However, this method is notefficient as it doubles the number of meters required. Moreover, if thereadings of the two meters are different, an error can be detected, butcannot be corrected, due to the ambiguity that the error can happen ateither of the two meters. The problem of ambiguity can be overcome byusing three meters or more on each cable and deciding on the correctreading with the majority rule, but this would increase the number ofmeters required by a huge amount. Another method is to use a meter onthe cable feeding a distribution node to measure a first sum of thepower of all cables fed from the distribution node and compare the firstsum with a second sum of the individual readings of the meters on thedifferent cables. However, this method would again detect that an errorexists but would not identify which meter is in error. In both methodsdiscussed above, examination of all the meters is required in order tospot the malfunctioning meter.

A third method is to compare the current electricity usage profile of acustomer with the customer's history, and try to guess that there is anerror in the meter reading if the average reading is different from theaverage reading of the history. In this case, correction of the error isperformed by estimating a correct reading from the history profile ofthe customer, and this must be agreed upon by both the electric utilitycompany and the customer. However, as expected, this method can haveinaccurate estimation and may cause a loss to both the electric utilitycompany and the customer.

Aspects of the disclosure provide a method, device, andcomputer-readable medium for detecting and correcting errors in readingsof a group of electricity meters. The group of electricity metersmeasure electric energy delivered by cables fed from the samedistribution node. The errors might result from tampering, attacking ormalfunctioning of the electricity meters. The method uses a codingscheme, and is based, with some modifications, on linearerror-correcting block codes that are used in communication systems todetect and correct errors in data packets transmitted from a transmitterto a receiver.

The method includes receiving first readings of regular meters measuringelectric energy delivered in each of a group of cables fed from a samedistribution node in an electric grid during a period of time, receivingsecond readings of check meters measuring electric energy delivered ineach of combinations of cables in the group of cables during the periodof time, the combinations of cables being formed based on a redundantmatrix in a generator matrix of a linear systematic block code, andcorrecting, in response to determining that at least one error has beendetected, the at least one error in the first readings and/or the secondreadings based on a syndrome vector of a reading vector that includesthe first readings and the second readings, the syndrome vector of thereading vector being computed using a decoding matrix that is obtainedby modifying a parity-check matrix of the linear systematic block code.

In an embodiment, the first readings of the regular meters correspondsto parameters regarding usage of the electric energy delivered in eachof the group of cables during the period of time, and the secondreadings of the check meters corresponds to parameters regarding usageof the electric energy delivered in each of combinations of cables inthe group of cables during the period of time.

In an embodiment, the syndrome vector of the reading vector is computedby multiplying the reading vector with the decoding matrix.

In an embodiment, modifying the parity-check matrix includes multiplyingelements of an identity matrix in the parity-check matrix with −1. Inanother embodiment, the decoding matrix is a modified decoding matrix,and modifying the parity-check matrix includes multiplying an element ofan identity matrix in the parity-check matrix with −1, and scaling anelement of the redundant matrix in the parity-check matrix using anestimated fraction of power loss in one of the group of cables withrespect to total electric energy delivered in the one cable.

In an embodiment, the method further includes determining that no errorhas been detected when values of elements of the syndrome vector arewithin a predetermined range, the predetermined range being determinedbased on accuracy of estimation of the power losses on the group ofcables. In an embodiment, in an embodiment, the correcting of the errorin the first readings and the second readings based on the syndromevector of a reading vector includes comparing the syndrome vector withrows in the decoding matrix to determine a position of a reading withthe error in the reading vector, and subtracting a value of the errorfrom the reading with the error, the value of the error beingapproximated using a value of an element in the syndrome vector that isnot in the predetermined range.

In an embodiment, each of the combinations of cables corresponds to acolumn of the redundant matrix, each element in the column correspondingto one of the group of cables, and each of the combinations of cablesincludes cables corresponding to non-zero elements in the column of theredundant matrix.

Aspects of the disclosure provide a device for correcting at least oneerror in readings of electricity meters. The device includes circuitrythat is configured to receive first readings of regular meters measuringelectric energy delivered in each of a group of cables fed from a samedistribution node in an electric grid during a period of time, receivesecond readings of check meters measuring electric energy delivered ineach of combinations of cables in the group of cables during the periodof time, the combinations of cables being formed based on a redundantmatrix in a generator matrix of a linear systematic block code, andcorrect, in response to determining that at least one error has beendetected, the at least one error in the first readings and the secondreadings based on a syndrome vector of a reading vector that includesthe first readings and the second readings, the syndrome vector of thereading vector being computed using a decoding matrix that is obtainedby modifying a parity-check matrix of the linear systematic block code.

Aspects of the disclosure provide a non-transitory computer-readablemedium having computer-readable instructions stored thereon. Theinstructions, when executed by one or more computers, cause the one ormore computers to perform a method for correcting at least one error inreadings of electricity meters. The method includes receiving firstreadings of regular meters measuring electric energy delivered in eachof a group of cables fed from a same distribution node in an electricgrid during a period of time, receiving second readings of check metersmeasuring electric energy delivered in each of combinations of cables inthe group of cables during the period of time, the combinations ofcables being formed based on a redundant matrix in a generator matrix ofa linear systematic block code, and correcting, in response todetermining that at least one error has been detected, the at least oneerror in the first readings and the second readings based on a syndromevector of a reading vector that includes the first readings and thesecond readings, the syndrome vector of the reading vector beingcomputed using a decoding matrix that is obtained by modifying aparity-check matrix of the linear systematic block code.

Error detecting and correcting codes were originally developed forbinary data transmission. The idea was to add redundant check bits toeach block of information bits (such a block is referred to as aninformation block). These redundant check bits carry some designedcharacteristics of the information bits. At the receiver, the check bitsare compared against the received information bits to determine whetherthere is an error. One of the very first codes was parity check codewhere a single bit was added to each block of information bits toindicate whether the information block contains an odd or even number ofones. This code helped in detecting an odd number of errors in a blockof information bits, but could neither correct the error nor was it ableto detect an even number of errors. Some other error detection codesalso appeared at that time, such as two-out-of-five code and repetitioncode.

Generally, in data communications, an error detecting and correctingcode is a system of rules to convert binary data into another form orrepresentation for communication through a channel or storage in amedium. A block code is any member of error-correcting codes that encodedata in blocks. A linear code is an error detecting and correcting codefor which any linear combination of code words is also a code word.Linear codes can be partitioned into block codes and convolutionalcodes, although turbo codes can be seen as a hybrid of these two types.Examples of linear block codes are Reed-Solomon codes, Hamming codes,Hadamard codes, Expander codes, Golay codes, and Reed-Muller codes.Detailed description of error detecting and correcting codes can befound in the work of S. Lin and D. J. Costello, Error Control Coding:Fundamentals and Applications, Pearson Prentice Hall, 2014, incorporatedherein by reference in its entirety.

Richard Hamming generalized the concept of two-out-of-five codes anddeveloped what is currently known as Hamming codes. Geometricallyspeaking, Hamming codes are based on the idea of adding redundant checkbits to each information block so as to generate different code wordsthat are far from each other by a minimum Hamming distance d_(min)=2n+1,where the Hamming distance between any two code words refers to thenumber of different bits between these two code words, and n is thenumber of errors that that the code can correct. This minimum distanceguarantees that there is a Hamming sphere surrounding each code wordwhich contains all binary words that are at a distance less than orequal to n far from the code word. Therefore, this minimum distanceguarantees that if errors occur to a maximum of n bits in any code word,the resulting binary word will still lie within the Hamming sphere ofthe original code word and will not cross the Hamming sphere of anothercode word, and hence upon receiving the erroneous code word, it ispossible to correct the code word to the original code word.

From a mathematical point of view, the check bits can be used to detecterrors in different bits by designing the code so that each single errorwould result in a different error shape in the check bits. For example,if M check bits are used, 2^(M) different binary combinations can beobtained which can be used to detect and correct single errors in2^(M)−1 positions. One is subtracted because one binary combination ofthe M check bits stands for no error case. Therefore, for an informationblock of length N augmented by MI check bits, the following conditionneeds to be satisfied,

N+M≦2_(M)−1,  (1)

or equivalently,

N≦2^(M) −M−1,  (2)

in order to be able to detect and correct a single error in the codeword.

One of the most famous binary Hamming codes is the (K, N)=(7,4) Hammingcode, which is a single-error correcting code, where the length of eachinformation block is N=4 and the length of the code word is K=7, andhence the number of check bits is M=3. These three check bits cancorrect a single error in any bit of 2^(M)−1=7 bits of the code word.

An example of (7,4) Hamming code is defined by a generator matrix Gshown as follows,

$\begin{matrix}{{G = {\begin{bmatrix}I_{4 \times 4} & H_{4 \times 3}\end{bmatrix} = \begin{bmatrix}1 & 0 & 0 & 0 & 1 & 1 & 0 \\0 & 1 & 0 & 0 & 1 & 0 & 1 \\0 & 0 & 1 & 0 & 0 & 1 & 1 \\0 & 0 & 0 & 1 & 1 & 1 & 1\end{bmatrix}}},} & (3)\end{matrix}$

where I_(4x4) is the identity matrix of size 4, and H_(4x3) is a matrixof size 4×3. Each information block generated from a source includesfour information bits which can be represented as a vector u=[u₁ u₂ u₃u₃], and each code word to be transmitted from the source to adestination includes seven bits which can be represented as a vectorv=[v₁ v₂ v₃ v₄ v₅ v₆ v₇]. Accordingly, a code word can be generatedusing the following expression corresponding to an information block,

v=u·G.  (4)

The generated code word is sent by a transmitter from the source to areceiver at the destination through a communication channel. At thereceiver, the received data block can be represented as r=v+e, where eis an error vector, which, for the case of a single error taking placein the transmission, contains all zeros except one bit being 1, andwhere the addition in this expression is modulo-2 addition. The modulo-2addition defines the following rule of addition operations: 0+1=1,0+0=0, and 1+1=0. Subsequently, the receiver uses a parity-check matrixPCH to detect whether there is an error in the received vector and thelocation of that error. Once the location of the error is known, theerror can be corrected by inverting the bit at that location. Theparity-check matrix PCH is orthogonal to the generator matrix G, andtherefore takes the form of,

$\begin{matrix}{{PCH} = {\begin{bmatrix}H_{4 \times 3}^{T} & I_{3 \times 3}\end{bmatrix}^{T} = {\begin{bmatrix}1 & 1 & 0 \\1 & 0 & 0 \\0 & 1 & 1 \\1 & 1 & 1 \\1 & 0 & 0 \\0 & 1 & 0 \\0 & 0 & 1\end{bmatrix}.}}} & (5)\end{matrix}$

It can be seen that G·PCH=0, where 0 is a zero matrix of size 4×3.

If the received code word r is received correctly, i.e., r=v, and e isall zeros, then multiplying r by the parity-check matrix PCH results in,

$\begin{matrix}\begin{matrix}{s = {r \cdot {PCH}}} \\{= {v \cdot {PCH}}} \\{= {u \cdot G \cdot {PCH}}} \\{= {u \cdot 0}} \\{= \begin{bmatrix}0 & 0 & 0\end{bmatrix}}\end{matrix} & (6)\end{matrix}$

where s is called a syndrome vector which indicates whether there is anerror or not and the location of that error. It can be seen that in caseof no error, the syndrome vector s is all zeros. On the other hand, ifthe received code word has a single error, i.e., r=v+e, the syndromevector s will be

$\begin{matrix}\begin{matrix}{s = {r \cdot {PCH}}} \\{= {\left( {{u \cdot G} + e} \right) \cdot {PCH}}} \\{= {e \cdot {PCH}}}\end{matrix} & (7)\end{matrix}$

which is not zero and equals to one row of the parity-check matrix PCH.For example, if there is an error in the fourth bit of the code word,i.e., e=[0 0 0 1 0 0 0], this will result in the syndrome vector s beingequal to the fourth row of the parity check matrix PCH. Since the rowsof the parity-check matrix PCH are different, then when decoding areceived data block r, by comparing the syndrome vector s to the rows ofthe matrix PCH, the location of the single error can be determined, andhence identify the error vector e. Once the error vector is identified,then the error can be corrected and the original code word v can beobtained from the received code word u and the error vector e as

v=u+e  (8)

where the addition is modulo-2 addition.

The above encoding and decoding scheme for correcting errors in acommunication channel with reference to Hamming code can be generalizedto linear block codes. In block coding, a binary information sequencefrom an information source is segmented into message blocks of fixedlength. Each message block, denoted by u, consists of N informationbits. An encoder transforms each input message block u into a binaryK-tuple v with K>N according to certain rules. This binary K-tuple v isreferred to as the code word of the message block u. Therefore, thereare 2^(N) distinct messages corresponding to 2^(N) distinct code words,and the set of 2^(N) distinct code words is referred to as a block code.A block code of length K and 2^(N) code words is called a linear (K, N)code if and only if its 2^(N) code words form a N-dimensional subspaceof the vector space of all the K-tuples over the field GF(2), FIG. 1shows an example of linear (K, N) code with N=4 and K=7, In FIG. 1, theleft column shows 2^(N)=16 messages and the right column shows 2^(N)=16code words.

For a linear (K, N) code, a matrix G, referred to as a generator matrix,can be found such that the code word v corresponding to the messageblock u can be obtained through the following expression,

v=u·G,  (9)

where the generator matrix G has a size of N×K, and rows of thegenerator matrix G are N linearly independent code words found in thelinear (K, N) code. For example, the linear (7, 4) code in FIG. 1 hasthe following matrix as a generator matrix,

$\begin{matrix}{G = {\begin{bmatrix}1 & 0 & 0 & 0 & 1 & 1 & 0 \\0 & 1 & 0 & 0 & 0 & 1 & 1 \\0 & 0 & 1 & 0 & 1 & 1 & 1 \\0 & 0 & 0 & 1 & 1 & 0 & 1\end{bmatrix}.}} & (10)\end{matrix}$

If u=[1 1 0 0] is the message to be encoded, its corresponding code word(as illustrated in FIG. 1) would be,

$\begin{matrix}\begin{matrix}{v = {u \cdot G}} \\{= {\begin{bmatrix}1 & 1 & 0 & 0\end{bmatrix} \cdot \begin{bmatrix}1 & 0 & 0 & 0 & 1 & 1 & 0 \\0 & 1 & 0 & 0 & 0 & 1 & 1 \\0 & 0 & 1 & 0 & 1 & 1 & 1 \\0 & 0 & 0 & 1 & 1 & 0 & 1\end{bmatrix}}} \\{= \begin{bmatrix}1 & 1 & 0 & 0 & 1 & 0 & 1\end{bmatrix}}\end{matrix} & (11)\end{matrix}$

It is noted that the addition operations performed in the encoding ordecoding process of a linear code are modulo-2 addition where 1+1=0.

In addition, for a linear (K, N) code having a generator matrix G thereexists a matrix PCH having a size of K×M where M=K−N, referred to as aparity-check matrix, corresponding to the generator matrix such that thefollowing expressions hold,

G·PCH=0,  (12)

v·PCH=0.  (13)

For example, a parity-check matrix of the linear (7, 4) code in FIG. 1corresponding to the generator matrix G in expression (10) is,

$\begin{matrix}{{PCH} = {\begin{bmatrix}1 & 1 & 0 \\0 & 1 & 1 \\1 & 1 & 1 \\1 & 0 & 1 \\1 & 0 & 0 \\0 & 1 & 0 \\0 & 0 & 1\end{bmatrix}.}} & (14)\end{matrix}$

A linear block code can have a systematic structure as shown in FIG. 1,where a code word is divided into two parts, the message part and theredundant checking part. The message part consists of N unalteredinformation bits while the redundant checking part consists of Mchecking bits. A linear block code with such a structure is referred toas a linear systematic block code. A linear systematic (K, N) code iscompletely specified by a N×K generator matrix G of the following form,

G=[I _(N×N) H _(N×M)],  (15)

where I_(NxN) denotes a K×K identity matrix, and H_(N×M), referred to asa redundant matrix in this disclosure, denotes the right part of thegenerator matrix G. Accordingly, if the generator matrix G of a linear(K, N) code is in the systematic form of expression (15), theparity-check matrix PCH may take the following form,

$\begin{matrix}{{{PCH} = \begin{bmatrix}H_{N \times M} \\I_{M \times M}\end{bmatrix}},} & (16)\end{matrix}$

where I_(MxM) denotes a M×M identity matrix. The generator matrix Ggiven in (10) and the parity-check matrix PCH given in (14) are both insystematic forms.

After a code word v is transmitted over a noisy channel, a vector r canbe received at the output of the channel, and the received vector r canbe expressed as,

r=v+e,  (17)

where e is a binary n-tuple and is referred to as an error vector orerror pattern. The 1's in the error vector e are the transmission errorscaused by the channel noise. Upon receiving r, a decoder at the receivercan first determine whether the received vector r contains transmissionerrors. If the presence of errors is detected, the decoder can takeactions to locate and correct the errors.

In order to detect the errors, the decoder can compute the followingM-tuple,

s=r·PCH,  (18)

where s is referred to as the syndrome of the received vector r.According to the expression (13), if r is a code word, then s=0, and ifr is not a code word, then s≠0. Therefore, when s≠0, the presence oferrors has been detected. However, when s=0, it is possible that theerrors in certain received vectors are not detectable. For example, whenthe error pattern e is identical to a nonzero code word, r is the sum oftwo code words which is a code word, consequently, s=r·PCH=0.

The syndrome s computed from the received vector r depends only on theerror pattern e, and not on the transmitted code word v, as shown below,

s=r·PCH=(v+e)·PCH=v·PCH+e·PCH=e·PCH.  (19)

Accordingly, because the parity-check matrix PCH is known to thedecoder, according to computed syndrome s, when s≠0, it is possible tocompute the error vector e. If the calculated error vector e is unique,the errors can be located and corrected; if the calculated error vectore is not unique, the errors cannot be corrected although the errors aredetectable.

When an error takes place in a received vector, the receiver is able todetermine the error vector and further correct the error. In an example,when an error takes place in a received vector, the error vector e willhave only one element with a nonzero value. According to expression(19), the computed syndrome s will be a row of the parity-check matrixPCH. As rows in the parity-check matrix PCH are distinct from eachother, the position of the row can be located based on the syndromevector s, and subsequently the position of the element with a nonzerovalue in the error vector e can be determined. As a result, the errorvector e is determined. Accordingly, the transmitted code word v can becomputed using the following expression,

v=r+e.  (20)

For example, for the linear (7, 4) code in FIG. 1 that has theparity-check matric PCH in (14), the code word v=[1 0 1 1 1 0 0] istransmitted and the received vector is r=[1 0 0 1 1 0 0] where the thirdbit has been corrupted. Upon receiving v, the receiver computes thesyndrome,

s=r·PCH  (21)

$\begin{matrix}{= {\begin{bmatrix}1 & 0 & 0 & 1 & 1 & 0 & 0\end{bmatrix} \cdot \begin{bmatrix}1 & 1 & 0 \\0 & 1 & 1 \\1 & 1 & 1 \\1 & 0 & 1 \\1 & 0 & 0 \\0 & 1 & 0 \\0 & 0 & 1\end{bmatrix}}} \\{= {\begin{bmatrix}1 & 1 & 1\end{bmatrix}.}}\end{matrix}$

The receiver can compare the syndrome vector [1 1 1] with the rows inthe parity-check matrix PCH to determine that the third bit of the errorvector e is 1 and the third bit of the received vector r has an error.Subsequently, the receiver can convert the third bit from of thereceived vector r to correct the error.

The minimum distance, denoted as d_(min), of a linear code determinesthe error detecting and error correcting capabilities of the linearcode. The minimum distance of a linear code is defined as the minimumHamming distance between any two code words of the linear code, andHamming distance is defined as the number of different places betweentwo binary sequences that have the same length. A block code withminimum distance d_(min) guarantees correcting all the error patterns oft=└(d_(min)−1)/2┘ or fewer errors, where └(d_(min)−1)/2┘ denotes thelargest integer no greater than (d_(min)−1)/2. In addition, a block codewith minimum distance d_(min) guarantees detecting all the errorpatterns of d_(min)−1 or fewer errors. For example, the linear (7, 4)code given in FIG. 1 has a minimum distance 3. Thus t=1 and the linear(7, 4) code is capable of correcting any error pattern of a single errorover a block of seven bits and detecting any error pattern of 2 errorsover a block of seven bits.

Hamming codes are the first class of linear codes devised for errorcorrection. For any positive integer M≧3, there exists a Hamming codewith the following parameters,

Code length: K=2^(M)−1;

Number of information bits: N=2^(M)−M−1;

Number of check bits: M=K−N

Error-correcting capability: t=1(d_(min)=3).

The code in FIG. 1 is an example of Hamming codes with K=7 and N=4.

FIG. 2 shows an electric energy transmission system 200 according tosome embodiments of the disclosure. The system 200 includes a pluralityof cables 210-213 fed from a same bus 220 in a distribution node. Thenumber of the plurality of cables 210-213 is denoted as N. The bus 220and the N cables 210-213 can be made of any conductible materialssuitable for electric energy transmission. The N cables 210-213 transmitelectric energy from the bus 220 to equipment (not shown) that consumeselectric energy or other electric energy transmission systems (notshown).

The N cables 210-213 can each have an electricity meter 230, referred toas a regular meter, that measures an amount of electric energy deliveredby each of the N cables 210-213 during a period of time and give areading, referred to as a regular reading, corresponding to the electricenergy delivered. The amount of electric energy delivered by a cable iduring the period of time is denoted as p_(i), for iε{1, 2, . . . , N},and p_(i) of all N cables can be represented as the vector p=[p₁ p₂ . .. p_(N)]. The regular reading of the regular meter 230 corresponding tothe cable i is denoted as x_(i), for iε{1, 2, . . . , N}. When allregular meters 230 are working properly, the regular reading x_(i) isequal to the amount of electric energy p_(i), thus x_(i)=p_(i), foriε{1, 2, . . . , N}.

In order to detect and correct errors in the readings of the regularmeters 230 measuring electric energy delivered by N cables 210-213, alinear (K, N) systematic block code, denoted as C, is selected to beused, and additional electricity meters 240, referred to as checkmeters, are installed in the system 200.

The number of information bits in the code word of the linear code C isequal to N that is the number of the regular meters or the cables in thesystem 200. The code C has a generator matrix G and a parity checkmatrix PCH, taking the following form, respectively,

$\begin{matrix}{{G = \begin{bmatrix}I_{N \times N} & H_{N \times M}\end{bmatrix}},} & (22) \\{{{PCH} = \begin{bmatrix}H_{N \times M} \\I_{M \times M}\end{bmatrix}},} & (23)\end{matrix}$

where I_(N×N) denotes a N×N identity matrix, I_(MxM) denotes a M×Midentity matrix, and H_(N×M) denotes the redundant matrix of thegenerator matrix G. In an example, the number of the cables in thesystem 200 is equal to 4, thus N=4, and a (7, 4) Hamming code is used.The Hamming code has a generator matrix and a parity check matrix listedbelow,

$\begin{matrix}{{G = {\begin{bmatrix}I_{4 \times 4} & H_{4 \times 3}\end{bmatrix} = \begin{bmatrix}1 & 0 & 0 & 0 & 1 & 1 & 0 \\0 & 1 & 0 & 0 & 1 & 0 & 1 \\0 & 0 & 1 & 0 & 0 & 1 & 1 \\0 & 0 & 0 & 1 & 1 & 1 & 1\end{bmatrix}}},} & (24) \\{{{PCH} = {\begin{bmatrix}H_{4 \times 3} \\I_{3 \times 3}\end{bmatrix} = \begin{bmatrix}1 & 1 & 0 \\1 & 0 & 1 \\0 & 1 & 1 \\1 & 1 & 1 \\1 & 0 & 0 \\0 & 1 & 0 \\0 & 0 & 1\end{bmatrix}}},} & (25)\end{matrix}$

where the redundant matrix H_(N×M) takes the following form,

$\begin{matrix}{H_{4 \times 3} = {\begin{bmatrix}1 & 1 & 0 \\1 & 0 & 1 \\0 & 1 & 1 \\1 & 1 & 1\end{bmatrix}.}} & (26)\end{matrix}$

The number of the check meters 240, denoted as M, is determined based onthe code C. Specifically, the number of the check meters 240 is equal tothe number of check bits of a code word of the code C, thus M=K−N. Thereading of each check meter 240, referred to as a check reading, isdenoted as x_(i), for iε{N+1, N+2, . . . , N+M}. Each check meter 240measures a sum of electric energy delivered by a selected combination ofcables in the N cables based on the code C. The selected combination ofcables is formed based on a column of the redundant matrix H_(N×M) ofthe code C. In other words, there are M check meters (or check readings)and there are M columns in the redundant matrix H_(N×M) of the code C;each check reading of the check meters 240 corresponds to a selectedcombination of cables that is formed based on each column of theredundant matrix H_(N×M). If all check meters are working properly andthus all check readings are correct, the check readings of the checkmeters 240, represented as a vector c=[x_(N+1) x_(N+2) . . . x_(N+M)],can be expressed as

c=p·H _(N×M).  (27)

For example, in the above example where the number of the cables 210-213in the system 200 is equal to 4, and the (7, 4) Hamming code is used,the redundant matrix H_(N×M) takes the form of expression (26), and thenumber of the check meters 240 M is equal to 3. Thus, there are threecheck meters corresponding to three check readings x₅, x₆, and x₇. Thefirst check meter measures a sum of electric energy delivered by a firstselected combination of cables including cables 210, 211 and 213corresponding to the nonzero elements in the first column of theredundant matrix H_(N×M) of expression (26); the second check metermeasures a sum of electric energy delivered by a second selectedcombination of cables including cables 210, 212 and 213 corresponding tothe nonzero elements in the second column of the redundant matrixH_(N×M) of expression (26); the third check meter measures a sum ofelectric energy delivered by a third selected combination of cablesincluding cables 211, 212 and 213 corresponding to the nonzero elementsin the third column of the redundant matrix H_(N×M) of expression (26).

After the regular readings and the check readings are obtained, theregular readings and the check readings together form a vector x=[x₁ x₂. . . x_(N+M)], referred to as a reading vector. For the situation thatall readings are correct, the reading vector can be denoted as x_(c).Based on the above description, the vector x_(c) can be expressed as,

x _(c) =p·G.  (28)

For the situation that there are errors with the readings, either checkreadings or regular readings, the reading vector can be denoted as{circumflex over (x)}, and can be expressed as

{circumflex over (x)}=x _(c) +e,  (29)

where e is an error vector, represented as e=[e₁ e₂ . . . e_(N+M)], eachelement e_(i) of which represents an error corresponding to the readingx_(i).

After the reading vector x=[x₁ x₂ . . . x_(N+M)] is obtained, in orderto detect and correct the errors in the readings of the electricitymeters, a process of decoding the reading vector x can be performed.

The first step of the decoding process is to detect the errors bycomputing a syndrome vector s of the reading vector x using a decodingmatrix D. The process of computing the syndrome vector s of the readingvector x is similar to the process of computing a syndrome vector in acommunication system using a linear systematic block code describedabove, however, the decoding matrix D used for decoding the readingvector x is different from the parity-check matrix PCH of expression(23) used for decoding in a communication system. The reason for thedifference is that in the communication system, binary vectors are usedand modulo-2 operation is applied, while when decoding the readingvector x, the readings in the reading vector x are not binary but realnumbers. As a result, the decoding matrix D takes a form of a modifiedparity-check matrix PCH as shown below,

$\begin{matrix}{D = {\begin{bmatrix}H_{N \times M} \\{- I_{M \times M}}\end{bmatrix}.}} & (30)\end{matrix}$

Compared with the parity-check matrix PCH in (23), the decoding matrix Dis the same as the parity-check matrix PCH in (23) except that theidentity matrix I_(M×M) is multiplied by a −1, It can be seen that G·D=0where regular addition operation is applied. In the example above wherethe (7, 4) Hamming code is used, an example of the decoding matrix D canbe expressed as,

$\begin{matrix}{D = {\begin{bmatrix}H_{4 \times 3} \\{- I_{3 \times 3}}\end{bmatrix} = {\begin{bmatrix}1 & 1 & 0 \\1 & 0 & 1 \\0 & 1 & 1 \\1 & 1 & 1 \\{- 1} & 0 & 0 \\0 & {- 1} & 0 \\0 & 0 & {- 1}\end{bmatrix}.}}} & (31)\end{matrix}$

To compute a syndrome vector s of the reading vector x using a decodingmatrix D, the reading vector x is multiplied by the decoding matrix D asshown below,

s=x·D.  (32)

For the case of no errors in the readings, the syndrome vector s will bezero, as shown below,

s=x·D=x _(c) ·D=p·G·D=0,  (33)

For the case that errors take place, the syndrome vector s will benonzero, as shown below,

s=x·D={circumflex over (x)}·D=(x _(c) +e)·D=e·D.  (34)

Therefore, if the computed syndrome vector s is equal to zero, no errorsare detected; otherwise, errors are detected. In case that only a singleerror is possible to take place in readings of the checking meters andregular meters, the syndrome vector s equal to zero means that there isno error in the readings. For cases that multiple errors take place,when the computed syndrome vector s is equal to zero, it is possiblethat undetectable errors exist.

When errors are detected, the second step of the decoding process is tolocate and correct the errors. In an example, a single error takes placein readings of the checking meters or regular meters, and according toexpression (34), the computed syndrome vector s is equal to one row ofthe decoding matrix D scaled by the value of the error. For example, inthe above example where the Hamming (7, 4) code is used, the decodingmatrix D takes the form of expression (31), and if there is a singleerror of e₃ in the reading of the third meter, e.g., e=[0 0 e₃ 0 0 0 0],the syndrome vector will be,

$\begin{matrix}\begin{matrix}{s = {e \cdot D}} \\{= {\begin{bmatrix}0 & 0 & e_{3} & 0 & 0 & 0 & 0\end{bmatrix} \cdot \begin{bmatrix}1 & 1 & 0 \\1 & 0 & 1 \\0 & 1 & 1 \\1 & 1 & 1 \\{- 1} & 0 & 0 \\0 & {- 1} & 0 \\0 & 0 & {- 1}\end{bmatrix}}} \\{= \left\lbrack \begin{matrix}0 & e_{3} & {\left. e_{3} \right\rbrack.}\end{matrix} \right.}\end{matrix} & (35)\end{matrix}$

It is noted that the value of the error e₃ can be positive or negativeand will appear in the syndrome vector with its sign. By comparing thepositions of the zeros of the syndrome vector to the positions of thezeros in the rows of the decoding matrix D, both the error location andthe error value can be found. Subsequently, the single error can becorrected by subtracting the error value from the reading of themalfunctioning meter, and the correct value of the malfunctioning metercan be obtained.

As can be seen in the above example where the Hamming (7, 4) code isused, the coding scheme described above is not efficient for scenarioswhere an electric energy transmission system includes only four cables,and the number of additional check meters, total of three, is very closeto the number of the regular meters. In communication systems, aparameter called code rate is defined to quantify the efficiency of acode. The code rate is defined as N/K for a linear (K, N) code. As thecode rate approaches 1, the code becomes more efficient. In the case ofthe Hamming (7,4) code, the code rate is N/K=4/7=0.571 which isconsidered to be inefficient. However, as shown in the expression (2),using a number of check meters M can serve a number of cablesN=2^(M)−M−1, which renders the code rate

${N/K} = {\frac{2^{M} - M - 1}{2^{M} - 1}.}$

It can be seen that by increasing the number of check meters M, the coderate approaches 1. For example, using M=5 can serve a number of cablesof N=26 giving a code rate of N/K=26/31=0.8387, Increasing M increasesthe code rate resulting in a more efficient coding scheme.

It is noted that the above description of the coding scheme does notconsider power losses in the cables 210-213 in the electricity energytransmission system 200 in FIG. 2. The coding scheme considering powerlosses is described below.

Typically, in the FIG. 2 example, there is a distance between the placewhere the regular meters 230 are installed and the place where the checkmeters 240 are installed. For example, the regular meters 230 can beinstalled at places close to customers or other transmission systems orequipment in an electric energy distribution system, while the checkmeters 240 can be installed at places close to the distribution node220, such as at a transformer. Thus, it is possible for the currenttransformers in the check meters to surround different groups of cables.Accordingly, differences in the readings between the regular meters 230and the check meters 240 will occur due to the power losses in thecables 210-213, which will appear as if there is a multiple-error case,although all the meters 230 and 240 might be reading correctly.Subsequently, the syndrome vector computed can give wrong results andthe coding scheme cannot be workable. In the above example where theHamming (7, 4) code is used, considering the power losses, the readingsof the meters 230 and 240 in case of all of the meters 230 and 240working properly can be expressed as follows,

$\begin{matrix}\begin{matrix}{x_{c} = {p \cdot \overset{\sim}{G}}} \\{= \left\lbrack \begin{matrix}p_{1} & p_{2} & p_{3} & {\left. p_{4} \right\rbrack \cdot \begin{bmatrix}{1 - \delta_{1}} & 0 & 0 & 0 & 1 & 1 & 0 \\0 & {1 - \delta_{2}} & 0 & 0 & 1 & 0 & 1 \\0 & 0 & {1 - \delta_{3}} & 0 & 0 & 1 & 1 \\0 & 0 & 0 & {1 - \delta_{4}} & 1 & 1 & 1\end{bmatrix}}\end{matrix} \right.} \\{{= \begin{bmatrix}{\left( {1 - \delta_{1}} \right)p_{1}} \\{\left( {1 - \delta_{2}} \right)p_{2}} \\{\left( {1 - \delta_{3}} \right)p_{3}} \\{\left( {1 - \delta_{4}} \right)p_{4}} \\{p_{1} + p_{2} + p_{4}} \\{p_{1} + p_{3} + p_{4}} \\{p_{2} + p_{3} + p_{4}}\end{bmatrix}^{T}},}\end{matrix} & (36)\end{matrix}$

where x_(c) is the reading vector in case of all meters 230 and 240working properly, the vector p represents the actual electric energydelivered by the cables 210-213 at the distribution node, the matrix{tilde over (G)}, referred to as a modified generator matrix, can beobtained by modifying the generator matrix G of expression (24), andδ_(i) for iε{1, 2, 3, 4} represents the actual fraction of power lossesin cable i with respect to the total electric energy delivered on thecable i. When decoding the reading vector x_(c) of expression (36), thesyndrome vector s can be computed as follows.

$\begin{matrix}\begin{matrix}\begin{matrix}{s = {x_{c} \cdot D}} \\{= {\begin{bmatrix}{\left( {1 - \delta_{1}} \right)p_{1}} \\{\left( {1 - \delta_{2}} \right)p_{2}} \\{\left( {1 - \delta_{3}} \right)p_{3}} \\{\left( {1 - \delta_{4}} \right)p_{4}} \\{p_{1} + p_{2} + p_{4}} \\{p_{1} + p_{3} + p_{4}} \\{p_{2} + p_{3} + p_{4}}\end{bmatrix}^{T} \cdot \begin{bmatrix}1 & 1 & 0 \\1 & 0 & 1 \\0 & 1 & 1 \\1 & 1 & 1 \\{- 1} & 0 & 0 \\0 & {- 1} & 0 \\0 & 0 & {- 1}\end{bmatrix}}} \\{{= \begin{bmatrix}{{{- \delta_{1}}p_{1}} - {\delta_{2}p_{2}} - {\delta_{4}p_{4}}} \\{{{- \delta_{1}}p_{1}} - {\delta_{3}p_{3}} - {\delta_{4}p_{4}}} \\{{{- \delta_{2}}p_{2}} - {\delta_{3}p_{3}} - {\delta_{4}p_{4}}}\end{bmatrix}^{T}},}\end{matrix} & \;\end{matrix} & (37)\end{matrix}$

where the decoding matrix D of expression (31) is used. The syndromevector s indicates incorrectly that one or more meters are readingincorrectly, although all of the meters 230 and 240 are readingcorrectly. This results from the fact that {tilde over (G)}·D≠0 becauseof the power losses in the cables 210-230.

To solve the problem with the power losses in the cables describedabove, a modified decoding matrix, denoted as {tilde over (D)}, is usedfor decoding the reading vector s. Specifically, the power losses ineach cable as a percentage of the total energy delivered by that cableis first estimated, e.g., based on the length, materials, etc. of thatcable. Then, the decoding matrix D is modified to be a modified decodingmatrix {tilde over (D)} based on the estimation to compensate for thepower losses. For example, in the example above where the Hemming (7,4)code is used, the estimated fractions of the energy losses in cables210-213 with respect to the total delivered energy by those cables canbe denoted as {circumflex over (δ)}₁, {circumflex over (δ)}₂,{circumflex over (δ)}₃, and {circumflex over (δ)}₄, respectively. Thenthe readings of the regular meters x₁, x₂, x₃, and x₄ can be scaled as

${\frac{1}{1 - {\hat{\delta}}_{1}}x_{1}},{\frac{1}{1 - {\hat{\delta}}_{2}}x_{2}},,{\frac{1}{1 - {\hat{\delta}}_{3}}x_{3}},,\mspace{14mu} {{and}\mspace{14mu} \frac{1}{1 - {\hat{\delta}}_{4}}x_{4}},,$

respectively, before they can be compared with the readings of the checkmeters. Therefore, the decoding matrix D in (31) may be modified as,

$\begin{matrix}{\overset{\sim}{D} = {\begin{bmatrix}\frac{1}{1 - {\hat{\delta}}_{1}} & \frac{1}{1 - {\hat{\delta}}_{1}} & 0 \\\frac{1}{1 - {\hat{\delta}}_{2}} & 0 & \frac{1}{1 - {\hat{\delta}}_{2}} \\0 & \frac{1}{1 - {\hat{\delta}}_{3}} & \frac{1}{1 - {\hat{\delta}}_{3}} \\\frac{1}{1 - {\hat{\delta}}_{4}} & \frac{1}{1 - {\hat{\delta}}_{4}} & \frac{1}{1 - {\hat{\delta}}_{4}} \\{- 1} & 0 & 0 \\0 & {- 1} & 0 \\0 & 0 & {- 1}\end{bmatrix}.}} & (38)\end{matrix}$

Generally, the modified decoding matrix {tilde over (D)} can be obtainedby scaling each element of the redundant matrix H_(N×M) in the decodingmatrix D of expression (30) using estimated fractions of power losses inthe cables with respect to the total delivered energy by those cables.

The modified decoding matrix {tilde over (D)} in (38) does not guaranteethat the syndrome vector of the reading vector x_(c) is going to be allzeros in case of all meters reading correctly, since the modification ofthe decoding matrix G is based on an estimation of the power losses andnot on the actual power losses. However, the modified decoding matrix{tilde over (D)} in (38) is able to reduce the values of the syndromevector to a very small value that is close to zero (depending on theaccuracy of the power losses estimation) when all meters are readingcorrectly. The syndrome vector, in this case, can be computed asfollows,

$\begin{matrix}\begin{matrix}\begin{matrix}{s = {x_{c} \cdot \overset{\sim}{D}}} \\{= {\begin{bmatrix}{\left( {1 - \delta_{1}} \right)p_{1}} \\{\left( {1 - \delta_{2}} \right)p_{2}} \\{\left( {1 - \delta_{3}} \right)p_{3}} \\{\left( {1 - \delta_{4}} \right)p_{4}} \\{p_{1} + p_{2} + p_{4}} \\{p_{1} + p_{3} + p_{4}} \\{p_{2} + p_{3} + p_{4}}\end{bmatrix}^{T} \cdot \begin{bmatrix}\frac{1}{1 - {\hat{\delta}}_{1}} & \frac{1}{1 - {\hat{\delta}}_{1}} & 0 \\\frac{1}{1 - {\hat{\delta}}_{2}} & 0 & \frac{1}{1 - {\hat{\delta}}_{2}} \\0 & \frac{1}{1 - {\hat{\delta}}_{3}} & \frac{1}{1 - {\hat{\delta}}_{3}} \\\frac{1}{1 - {\hat{\delta}}_{4}} & \frac{1}{1 - {\hat{\delta}}_{4}} & \frac{1}{1 - {\hat{\delta}}_{4}} \\{- 1} & 0 & 0 \\0 & {- 1} & 0 \\0 & 0 & {- 1}\end{bmatrix}}} \\{= {\begin{bmatrix}{{\frac{{\hat{\delta}}_{1} - \delta_{1}}{1 - {\hat{\delta}}_{1}}p_{1}} + {\frac{{\hat{\delta}}_{2} - \delta_{2}}{1 - {\hat{\delta}}_{2}}p_{2}} + {\frac{{\hat{\delta}}_{4} - \delta_{4}}{1 - {\hat{\delta}}_{4}}p_{4}}} \\{{\frac{{\hat{\delta}}_{1} - \delta_{1}}{1 - {\hat{\delta}}_{1}}p_{1}} + {\frac{{\hat{\delta}}_{3} - \delta_{3}}{1 - {\hat{\delta}}_{3}}p_{3}} + {\frac{{\hat{\delta}}_{4} - \delta_{4}}{1 - {\hat{\delta}}_{4}}p_{4}}} \\{{\frac{{\hat{\delta}}_{2} - \delta_{2}}{1 - {\hat{\delta}}_{2}}p_{2}} + {\frac{{\hat{\delta}}_{3} - \delta_{3}}{1 - {\hat{\delta}}_{3}}p_{3}} + {\frac{{\hat{\delta}}_{4} - \delta_{4}}{1 - {\hat{\delta}}_{4}}p_{4}}}\end{bmatrix}^{T}.}}\end{matrix} & \;\end{matrix} & (39)\end{matrix}$

It can be seen from (38) that if the estimation of the power losses iscorrect, i.e., {circumflex over (δ)}_(i)−δ_(i), for iε{1, 2, 3, 4}, thenthe syndrome vector will have all zero elements. These estimations ofthe power losses can be examined in the beginning while installing theenergy transmission system 200 until the correct values of the lossesare obtained and the syndrome vector is a zero vector when all themeters are reading correctly. When changes in the physicalcharacteristics of the cable take place later, the estimation can stillbe very close to the actual losses.

If the estimation of the power losses is not exactly equal to the actuallosses, this will result in a syndrome vector with very small elements;the values of the elements depend on the accuracy of the estimation. Asan example, if the actual fraction of energy loss in all the cables is1.8% and is estimated to be 2%, then the syndrome vector will be

$\begin{matrix}{s = {\begin{bmatrix}{0.00204\left( {p_{1} + p_{2} + p_{4}} \right)} \\{0.00204\left( {p_{1} + p_{3} + p_{4}} \right)} \\{0.00204\left( {p_{2} + p_{3} + p_{4}} \right)}\end{bmatrix}^{T}.}} & (40)\end{matrix}$

On the other hand, in case there is an error in the reading of one ofthe meters 230 and 240, this error will be magnified in the syndromevector. For example, in the above example where the Hamming (7, 4) codeis used, and if there is an error of e₃ in the reading of the thirdmeter, e.g., e=[0 0 e₃ 0 0 0 0], the syndrome vector will be,

$\begin{matrix}\begin{matrix}{s = {\hat{x} \cdot \overset{\sim}{D}}} \\{= {\left( {x_{c} + e} \right) \cdot \overset{\sim}{D}}} \\{= {{x_{c} \cdot \overset{\sim}{D}} + {e \cdot \overset{\sim}{D}}}} \\{{= \begin{bmatrix}{{\frac{{\hat{\delta}}_{1} - \delta_{1}}{1 - {\hat{\delta}}_{1}}p_{1}} + {\frac{{\hat{\delta}}_{2} - \delta_{2}}{1 - {\hat{\delta}}_{2}}p_{2}} + {\frac{{\hat{\delta}}_{4} - \delta_{4}}{1 - {\hat{\delta}}_{4}}p_{4}}} \\{{\frac{{\hat{\delta}}_{1} - \delta_{1}}{1 - {\hat{\delta}}_{1}}p_{1}} + {\frac{{\hat{\delta}}_{3} - \delta_{3}}{1 - {\hat{\delta}}_{3}}p_{3}} + {\frac{{\hat{\delta}}_{4} - \delta_{4}}{1 - {\hat{\delta}}_{4}}p_{4}} + {\frac{1}{1 - {\hat{\delta}}_{3}}e_{3}}} \\{{\frac{{\hat{\delta}}_{2} - \delta_{2}}{1 - {\hat{\delta}}_{2}}p_{2}} + {\frac{{\hat{\delta}}_{3} - \delta_{3}}{1 - {\hat{\delta}}_{3}}p_{3}} + {\frac{{\hat{\delta}}_{4} - \delta_{4}}{1 - {\hat{\delta}}_{4}}p_{4}} + {\frac{1}{1 - {\hat{\delta}}_{3}}e_{3}}}\end{bmatrix}^{T}},}\end{matrix} & (41)\end{matrix}$

where elements with the error e₃ are usually larger than other elements.Therefore, in order to determine whether there is a meter with anincorrect reading, the elements of the syndrome vector s that lie withina small range around zero, e.g., ε≧s_(i)≧−ε, can be approximated tozero. The range, ε, is determined based on the expected accuracy of theestimation of the power losses. Once the above values are approximatedto zero, the remaining non-zero elements and their position in thesyndrome vector can be used to determine which meter has an error andthe approximate value of the error.

In various embodiments, a predetermined range can be used fordetermination of whether there is a meter with an incorrect reading. Thepredetermined range is determined based on an expected accuracy of theestimation of the power losses in cables with meters whose readings aremonitored using the coding scheme. In the example presented byexpression (40), the predetermined range can be from −6 kilowatt-hour(kWh) to +6 kWh. This predetermined range corresponds to the amount ofelectric energy delivered by each cable being in a range of 1000 kWh inthe example represented by expression (40). When all the elements of thesyndrome vector s lie within the predetermined range, it is determinedthat there are no errors in the reading vector.

Although the coding scheme considering power losses in cables describedabove uses the Hamming (7, 4) code, the scope of the disclosure is notlimited to the Hamming (7, 4) code, and the coding scheme consideringpower losses can be applied to other linear systematic block codes usedfor detecting and correcting errors in readings of electricity meters.

Two exemplary linear systematic block codes capable of correctingmultiple errors are described below. Generally, designing a code that iscapable of directly finding the locations and values of multiple errorscan be done by increasing the number of check bits to cover all thedifferent combinations of errors. Specifically, for N regular meters andM check meters, in order to be able to correct up to T terrors, M mustsatisfy the following inequality, known as Hamming bound,

$\begin{matrix}{{{2^{M} \geq {\sum\limits_{i = 0}^{i = T}\; \begin{pmatrix}{N + M} \\i\end{pmatrix}}},{where}}\; \begin{pmatrix}{N + M} \\i\end{pmatrix}} & (42)\end{matrix}$

represents the number of all combinations of i errors, for iε0, 1, 2, .. . , T. In case of codes capable of correcting up to two-errors, i.e.,T=2 in (42), the Hamming bound can be simplified to a simple relationbetween the number of regular meters N and the number of check meters M,as, N≦−M+√{square root over (2^(M)−1)}.

A code that satisfies the Hamming bound in expression (42) with equalityis called a perfect code. A perfect code also means that the Hammingspheres around each code word not only are non-overlapping but they alsoexhaust all the 2 K possible binary words of length K. The Hammingsphere around each code word is the binary words of length N that liewithin a Hamming distance t from the code word.

In the work of S. Lin and D. J. Costello, Error Control Coding:Fundamentals and Applications, Pearson Prentice Hall, 2014, severalcodes have been developed to detect and correct multiple errors. Theapplication of correcting errors in readings of electricity metersrequires the original code to be binary so that the measurements arejust the sum of energies delivered by a group of cables. Some binarycodes used to detect and correct multiple errors are Reed-Muller (RM)codes, Golay code, and binary BCH codes.

In general, these codes, except for Golay code, are not systematic.Although non-systematic codes can be transformed into systematic codes,being non-systematic allows the use of low complexity decodingalgorithms without having to store large syndrome tables. Converting thecodes into systematic codes has the disadvantage of losing the codestructure that allows low complexity decoding process, which might causedelay problems especially in applications related to high speed datatransmission over a communication channel. However, correcting errors inelectricity meters is not delay-limited, and hence the decodingcomplexity that results from converting the codes into systematic codeswill not affect the operation of the proposed system.

On the other hand, although the application of detecting and correctingerrors in the readings of smart meter can work well with non-systematiccodes, the practical implementation would be easier and less expensivewith systematic codes. This is due to the fact that in systematic codes,due to the identity matrix, most of the meters will be regular meters tomeasure the energy delivered by only one cable, while withnon-systematic codes, all the meters will be check meters. Therefore,transforming these codes into systematic codes may be needed, which canbe done by row and column operations on the code generating matrices.Shown below are an example of (23, 12) Golay code as a systematic codeand another example of a Reed-Muller code as a non-systematic code thatcan be converted into a systematic code.

A (23, 12) Golay code is a three-error correcting perfect code. Theminimum distance of the (23, 12) Golay code was shown to be 7. Beingperfect means that the (23, 12) Golay code satisfies the Hamming boundin (42) with equality. As can be seen, the (23, 12) Golay code has acode length of K=23, data length of N=12, and the number of check bitsis M=11. Besides the hamming code, the (23, 12) Golay code is the onlyknown perfect code. The (23, 12) Golay code can be extended to (24, 12)Golay code by adding an overall parity check bit. In this case the (24,12) Golay code is no longer a perfect code and has a minimum distance of8, which can be used to correct up to three errors and detect errorpatterns with four errors.

The (24, 12) Golay code generator matrix takes the same structure of asystematic linear block code, and so does the decoding matrix.Therefore, in the application of correcting errors in readings ofelectricity meters, when the (24, 12) Golay code is used, the same stepsdescribed above for the Hamming code example are performed. For example,the steps of using the (24, 12) Golay code for correcting errors inreadings of electricity meters can include receiving the reading vectorof the regular meters and check meters, calculating a syndrome vector ofthe reading vectors by multiplying the reading vectors with a decodingmatrix that is a modified parity-check matrix of the (24, 12) Golaycode, using the syndrome vector to decide an error vector, andsubsequently correcting the errors. However, in one example, in order todecide an error vector, a syndrome lookup table is used. The syndromelookup table will be of a larger size in order to cover all the cases ofmultiple errors up to three errors. A syndrome lookup table representsmapping relationship between syndrome vectors and error vectorsindicating error patterns. By using the syndrome lookup table, when asyndrome vector is calculated, a corresponding error vector can be foundaccordingly.

In another example, because the structure of the (24, 12) Golay codeallows the use of a lower complexity decoding algorithm in the binarycase, the lookup table is not used. The decoding algorithm is introducedin the fourth chapter of the work of S. Lin and D. J. Costello, ErrorControl Coding: Fundamentals and Applications, Pearson Prentice Hall,2014. This algorithm can be used by first converting the syndrome vectorto a binary vector by leaving any zero value (small value in case ofenergy losses) as zero and converting any nonzero value into one, thenusing the decoding algorithm to determine the locations of the errors,and finally using the values of the original syndrome vector to correctthe errors.

Reed-Muller (RM) codes were first discovered by David E. Muller in 1954,while the first majority-logic decoding algorithm for these codes wasdevised by Irwin S. Reed in the same year. RM codes are defined in termsof two integer parameters in and r, where 0≦r≦m. RM(r, m) is a code thathas a length K=2^(m), minimum distance d_(min)=2^(m-r), and data length

$m = {1 + \begin{pmatrix}m \\1\end{pmatrix} + \begin{pmatrix}m \\2\end{pmatrix} + \ldots + {\begin{pmatrix}m \\r\end{pmatrix}.}}$

The structure of the RM codes allows the use of the low computationalcomplexity majority-logic decoding algorithm. This code structure is notsystematic, and after transforming the code structure into a systematicstructure, it may not be possible to use the majority-logic decodingalgorithm, and it may be needed to use lookup tables with highercomputational complexity.

However, converting the RM codes into a systematic form has theadvantage of having most of the meters be regular meters (capable ofmeasuring the energy in one cable), while a few meters be check meters(capable of measuring the sum of energies in a group of cables). Thiscan reduce the cost and the complexity of the physical connections. Inthe application of correcting errors in readings of electricity meters,computational complexity is not a critical issue since the applicationof detecting and correcting errors in smart meters is not delay limited.Therefore, it may be needed to transform the RM codes into a systematicform.

Here one example of Reed-Muller codes, RM(2, 5) is shown. This code hasa length of K=32, data length of N=16, and minimum distance of dmin=8,and hence this code is capable of correcting up to three simultaneouserrors in the readings of 32 meters. The generator matrix of such a codecan be written as below.

$G = \begin{pmatrix}1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 \\0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 \\0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 \\0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1\end{pmatrix}$

Using column and row operations, the generator matrix of such a code canbe transformed into a systematic generator matrix G=[I H], where thematrix H is shown below.

$H = \begin{pmatrix}1 & 1 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 1 \\0 & 1 & 1 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 1 \\0 & 0 & 1 & 1 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 0 & 1 \\0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 \\1 & 1 & 1 & 1 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 0 \\1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 1 & 1 & 1 \\1 & 0 & 1 & 1 & 0 & 0 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0 \\1 & 0 & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 \\0 & 1 & 0 & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\0 & 0 & 1 & 0 & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 \\0 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 1 & 0 & 1 \\0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 1 & 1 \\1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 0 \\0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 0 & 0 \\0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 0 \\1 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 1 & 1\end{pmatrix}$

In addition, for the purpose of detecting and correcting errors in thereadings of the electricity meters, the decoding matrix should bewritten as D=[H^(T)−I]^(T). In the application of correcting errors inreadings of electricity meters, when the RM codes are used, the similarsteps described above for the Hamming code example are performed. Forexample, the steps of using RM codes for correcting errors in readingsof electricity meters can include receiving the reading vector of theregular meters and check meters, calculating a syndrome vector of thereading vectors by multiplying the reading vectors with the decodingmatrix D=[H^(T)−I]^(T), using the syndrome vector to decide an errorvector, and subsequently correcting the errors. However, in order todecide an error vector, in one example, the syndrome look-up tables areused as a general method of detecting and correcting the errors. Inanother example, a simpler algorithm that is based on majority-logicdecoding was introduced in the paper, P. Hauck, M. Huber, J. Bertram, D.Brauchle, and S. Zeische, “Efficient majority-logic decoding ofshort-length reed-muller codes at information position s,” IEEETransactions on Communications, vol. 61, no. 3, pp. 930-938, March 2013,which is incorporated herein by reference in its entirety. Thisalgorithm works for short length RM codes r≦m/2 where systematicencoding is used.

Aspects of the disclosure provide some examples of measuring electricenergy delivered in one or more cables using electricity meters, asdescribed below.

For measuring an amount of electric energy delivered by one cable, sincethe first AC Kilowatt-hour electricity meter that was invented by OttoBlathy in 1889, until the development of the currently used ACKilowatt-hour meters, the basic idea of measuring the amount of energydelivered by a cable has been to measure the voltage and the current,and to multiply them to calculate the electric energy. In case ofelectromechanical meters, a rotating disc with an angular speedproportional to the power is used, where the number of rotations of thedisc will be proportional to the delivered energy. On the other hand, incase of electronic meters, a signal processing device is used to sample,quantize, and calculate the amount of delivered electric energy. In somecases where the load is small, i.e., residential load, the meter can beinstalled directly on the line connecting the source to the load, whileif the load is large (usually greater than 200 Ampere), currenttransformers are used to measure a scaled version of the current on thecable and allow the meter to be placed in another location that might befar from the cable, e.g., a control room. A current transformer producesa reduced current accurately proportional to the current that is toohigh to apply directly to measuring instruments. In addition, a voltagetransformer is used to measure a scaled version of the voltage. Avoltage transformer produces a reduced voltage accurately proportionalto the voltage that is too high to apply directly to measuringinstruments. Subsequently, the meter uses these scaled versions of thecurrent and voltage to calculate the delivered electric energy.

For measuring an amount of electric energy delivered by a group ofcables fed from the same distribution node, a current transformer thatsurrounds the group can be used. In addition, a voltage transformer canbe used on the feeder that feeds the distribution node, because all thecables have the same voltage. Specifically, in the coding schemedescribed above, in order to measure the electric energy delivered bydifferent groups of cables using check meters, where some cables aremembers of several different groups, one group of cables can by bundledat a certain point and surrounded with a current transformer, and thenanother group of cables can be bundled at a different point andsurrounded with another current transformer, and so on for the othergroups of cables. In addition, one voltage transformer can be used tomeasure the voltage at the distribution node that is the same for allcables. Based on the currents and voltage measured by the currenttransformers and voltage transformer, amounts of electric energydelivered by different groups of cables can be computed at the checkmeters.

FIG. 3 shows an exemplary configuration of an electric energytransmission system 300 for measuring electric energy delivered bydifferent groups of cables according to some embodiments of thedisclosure. In the system 300, the coding scheme described above isimplemented. As shown, the system 300 includes four cables 301-304 thatare fed from a bus 305 at a distribution node, and a cable 306 thatfeeds to the bus 305. In order to measure the voltage on the cables301-304, a voltage transformer 321 is installed on the cable 306 in thesystem 300 to obtain a voltage signal for voltage measurement. Inaddition, three current transformers 311-313 are installed at threedifferent locations, point A, point B, and point C, to obtain signalsfor current measurement of different combinations of cables at differentpoints. At different locations, the cables 301-304 are combined intodifferent groups according to a redundant matrix in a generator matrixof a linear systematic block code used in the coding scheme.Specifically, at point A, the cables 301-303 are bundled together toform a first combination of cables that pass through the currenttransformer 311. At point B, the cables 301, 303, and 304 are bundledtogether to form a second combination of cables that pass through thecurrent transformer 312. At point C, the cables 302-303 are bundledtogether to form a third combination of cables that pass through thecurrent transformer 313.

One practical problem that might appear here is the electromagneticinterference affecting each current transformer from the cables that donot belong to the measured group by that current transformer. Thiselectromagnetic effect might appear because of the proximity of cablesat the measurement area. However, by carefully insulating the currenttransformers from the other cables, it is possible to avoid or reducethe effect of such interference.

FIG. 4 shows an electricity metering system 400 using the coding schemedescribed above according to some embodiments of the disclosure. Thesystem 400 includes a plurality of check meters 401, a plurality ofregular meters 402, communication networks 410 and a reading processingsystem 420. The system 400 uses the coding scheme described above todetect and correct errors in the readings of electricity metersmeasuring electric energy delivered by cables in an electric grid, and alinear systematic code is used in the coding scheme.

The electric grid can include multiple electric energy transmissionsystems similar to the system 200 in FIG. 2 where a plurality of cablesare fed from a same distribution node delivering electric energy tocustomers or other electric energy transmission systems. In theseelectric energy transmission systems, the check meters 401 can beinstalled near a distribution node and each measure a sum of energydelivered by a combination of cables. The combinations of cables areformed based on a generator matrix of the linear systematic code. Inaddition, in these electric energy transmission systems, regular meterscan be installed to measure energy delivered by each cable at the otherends of the cables that are far from the distribution nodes but close tocustomer premises or other transmission systems. Readings of checkmeters and regular meters in a same transmission system measuringelectric energy delivered during a period of time forms a readingvector.

In an embodiment, as shown in FIG. 4, the meters 401 and 402 areconfigured to transmit the readings to the reading processing system 420via the communication networks 410. The meters 401 and 402 can eachinclude communication circuitry (not shown). The communication circuitrycan be configured to provide communication channels for components inthe meters 401 and 402 to communicate with the communication networks410. The communication networks 410 can include WLANs, wired-LANs,wireless cellular networks, Internet, wide-area networks, and the like.Accordingly, the communication circuitry and the communication networks410 can operate with various communication protocols, such as WiFi,Bluetooth, Internet protocols, wireless cellular network protocols (e.g.general packet radio service (GPRS), wideband code division multipleaccess (WCDMA), Long-Term Evolution (LTE)), any other communicationprotocols, or any combination thereof.

The reading processing system 420 is configured to receive the readingsof the meters 401 and 402 and decode the reading vector to detect andcorrect errors in the readings. The reading processing system 420 caninclude a reading collector 421, a database 422, and a reading vectordecoding device (decoder) 423.

The reading collector 421 is configured to communicate with the checkmeters 401 and the regular meters 402 to receive the readings of themeters 401 and 402, and transmit the readings to the database 422. Thereading collector 421 can include communication circuitry (not shown)that is configured to provide communication channels for the readingcollector 421 to communicate with the communication networks 410.Accordingly, the communication circuitry in the reading collector 421can operate with various communication protocols, such as WiFi,Bluetooth, Internet protocols, wireless cellular network protocols (e.g.GPRS), WCDMA, Long-Term Evolution (LTE), any other communicationprotocols, or any combination thereof.

The database 422 is configured to store the readings of the meters 401and 402 into a memory (not shown) and transmit the readings of themeters 401 and 402 to the reading vector decoding device 423 whenqueried by the reading vector decoding device 423. The memory can use avariety of computer storage media in various embodiments, such as randomaccess memory (RAM), read-only memory (ROM), electrically erasableprogrammable read-only memory (EEPROM), flash memory, compact discread-only memory (CD-ROM), digital versatile disk (DVD) or other opticaldisk storage, magnetic disk storage, and the like.

The reading vector decoding device 423 is configured to receive readingsof the meters 401 and 402 and decode the reading vector to detect andcorrect errors in the readings. The reading vector decoding device 423can first receive from the database 422 the readings of the check meters401 and regular meters 402 that are installed in a same transmissionsystem, the readings being amounts of electric energy measured duringthe same time period. These received readings form the reading vectoraccording to the coding scheme described above.

Then, the reading vector decoding device 423 can correct errors in thereceived readings based on a syndrome vector s of the reading vector.The syndrome vector of the reading vector can be computed using adecoding matrix D. The decoding matrix D is obtained by modifying aparity-check matrix PCH of the linear systematic block code. Thedecoding matrix D may be a modified decoding matrix {tilde over (D)} inwhich elements in the redundant matrix of the decoding matrix D havebeen scaled using estimated fractions of power losses in the cables inthe transmission system where the received readings are obtained.Specifically, the reading vector decoding device 423 multiplies thereading vector with the modified decoding matrix {tilde over (D)} tocompute the syndrome vector s of the reading vector. Considering powerlosses, if each element in the syndrome vector is equal to zero, or isin a predetermined range, the reading vector decoding device 423determines that there is no error in the received readings; otherwise,it is determined that there is one or more errors in the receivedreadings. The size of the predetermined range depends on the accuracy ofthe estimation of the power losses.

When errors are detected, the reading vector decoding device 423 cancorrect the errors based on the computed syndrome vector s, for example,by comparing the syndrome vector s with rows in the decoding matrix D tolocate position of the error in the reading vector. In an example, anerror takes place, power losses are considered, and the modifieddecoding matrix {tilde over (D)} is used. The reading vector decodingdevice 423 compares positions of elements in the syndrome vector s thatis equal to zero or in a predetermined range with the positions of zerosin each row in the decoding matrix D (not the modified decoding matrix{tilde over (D)}), position of a row whose elements with zero valueshave the same positions as elements with values equal or close to zeroin the syndrome vector s is determined to be the position correspondingto the position of the error in the reading vector.

Subsequently, the reading vector decoding device 423 can correct theerror in the reading vector based on the position determined above. Thevalues of the elements in the syndrome vector s exclude the elementswith values equal to or close to zero can be used to proximate the valueof the error. Accordingly, the value of error can be subtracted from areading with the error to obtain the true value of the reading.

As a result, the corrected readings of the regular meters can be used asbilling information for charging customers, thus avoiding wrong readingsof electricity meters and disputes between customers and utilitycompanies. In addition, the reading vector decoding device 423 cangenerate a report indicating the errors that have been corrected. Forexample, the report can include an identification (ID) indicating acheck meter or a regular meter that has an incorrect reading. Further,the report can include the value of the error that has been subtractedfrom the readings with the error. The report can include a plurality ofentries each corresponding to a check meter or a regular meter having anerror in its readings during a certain billing cycle. In an example, thereport may be transmitted, for example, via a network, to a maintenancedepartment in a power grid operator and maintenance staff can locate andexamine the meters with incorrect readings.

In an embodiment, the reading processing system 420 is implemented as adistributed system. For example, the elements in the reading processingsystem 420 can be implemented separately in different computers and theelements can communicate with each other through various networks. Inanother embodiment, the reading processing system 420 can be integratedinto one physical device, such as a computer.

In alternative embodiments, the meters 401 and 402 are configured togenerate and send parameters regarding the usage of electricity, insteadof readings of amounts of delivered electric energy, to the readingprocessing system 420. The parameters regarding the usage of electricitycan be, for example, current and voltage values sampled at differenttime instant from the cables in an electric energy transmission systemthat can be used to compute amounts of energy delivered in the cables.The reading collector 421 can receive such parameters and store theparameters in the database 422. Accordingly, the reading vector decodingdevice 423 can be configured to first receive the parameters regardingthe usage of the electricity from the database 422 and then computereadings corresponding to amounts of electric energy delivered in thecables for a period of time. These computed readings can form a readingvector used in the subsequent reading vector decoding process at thereading vector decoding device 423.

In other embodiments, the readings or parameters generated at the meters401 and 402 can be transmitted to the reading processing system 420using other methods different from using the communication networks 410in FIG. 4. For example, the readings or parameters can be collectedusing a portable device that is taken to the vicinity of the meters 401and 402 and stored in a storage media, such as a flash memory, connectedto the portable equipment. The portable device, for example, can be anydevice using various Automatic Meter Reading (AMR) technologies tocollect consumption data from electricity meters. Then, the storagemedia is connected to the reading collector 421 and the readings can beread from the storage media and stored into the database 422.

FIG. 5 shows a process 500 for detecting and correcting errors inreadings of electricity meters measuring electric energy delivered in agroup of cables fed from a same distribution node in an electric gridaccording to an embodiment of the disclosure. The process 500 uses thecoding scheme described above and a linear systematic code is used inthe process 500. The process starts at S501 and proceeds to S510.

At S510, readings of regular meters that measure electric energydelivered in each cable are received.

At S520, readings of check meters that each measures electric energydelivered in a combination of cables in the group of cables arereceived. The combinations of cables are formed based on a redundantmatrix in a generator matrix of the linear systematic code.

At S530, errors in the received readings can be corrected based on asyndrome vector of a reading vector that includes the readings of theregular meters and the readings of the check meters. The syndrome vectoris computed using a decoding matrix. The decoding matrix is obtained bymodifying a parity-check matrix of the linear systematic code. Whenpower losses in the cables are considered, a modified decoding matrix isused for computing the syndrome vector. Specifically, the reading vectoris multiplied by the decoding matrix or the modified matrix to computethe syndrome vector. When the syndrome vector is a zero vector or valuesof elements in the syndrome vector are in a predetermined range, it canbe determined that there is no error in the received readings;otherwise, it is determined that errors are detected in the readings ofthe check meters or the regular meters.

Errors in the readings can be corrected based on the computed syndromevector. In an example, position of an error in the reading vector can bedetermined by comparing the syndrome vector with rows of the decodingmatrix, and the values of elements in the syndrome vector that are notin the predetermined range can be used to proximate the value of theerror. Subsequently, the value of the error can be subtracted from areading with the error to obtain the true value of the reading. Theprocess proceeds to S599 and terminates at S599.

The coding scheme, the reading processing system 420 and the process 500described above can be implemented with any suitable software, hardware,or a hardware/software combination. In one embodiment, the codingscheme, the reading processing system 420 and the process 500 can beimplemented as an application program comprised of computer-executableinstructions that can be stored in a computer-readable media and can runon one or more computers. In one embodiment, the coding scheme, thereading processing system 420 and the process 500 can be implemented incombination with other programs, such as operating systems and programmodules of other applications, or can be implemented as a combination ofhardware and software.

Each of the functions of the described embodiments including the readingvector decoding device 423 may be implemented by one or more processingcircuits. A processing circuit includes a programmed processor as aprocessor includes circuitry. A processing circuit may also includedevices such as an application specific integrated circuit (ASIC) andconventional circuit components arranged to perform the recitedfunctions.

In addition, the coding scheme, the reading processing system 420 andthe process 500 described above can be implemented with various suitablecomputer system configurations, such as single-processor ormultiprocessor computer systems, minicomputers, mainframe computers, aswell as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like.

Further, the coding scheme, the reading processing system 420 and theprocess 500 described above can also be implemented in distributedcomputing environments. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices,and certain functions can be performed by remote processing devices thatare linked to local processing devices through a communications network.

Still further, information such as computer-readable instructions, datastructures, program modules or other data can be stored in a variety ofcomputer storage media, such as random access memory (RAM), read-onlymemory (ROM), electrically erasable programmable read-only memory(EEPROM), flash memory, compact disc read-only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage, and the like.

FIG. 6 shows an exemplary computer 600 for implementing some or all ofthe aspects of the disclosure including the coding scheme, the readingprocessing system 420, and the process 500 described above according tosome embodiments of the disclosure. In FIG. 6, the computer 600 includesa central processing unit CPU 601 which may be part of the circuitry forimplementing the reading vector decoding device 423 of FIG. 4 and canperform the operations and processes described above. The data andinstructions may be stored in a memory 602. These data and instructionsmay also be stored on a storage medium disk 604 such as a hard drive(HDD) or portable storage medium or may be stored remotely. Further, theaspects of the disclosure are not limited by the form of thecomputer-readable media on which the instructions of the inventiveprocess are stored. For example, the instructions may be stored on CDs,DVDs, in FLASH memory, RAM, ROM, PROM, EPROM, EEPROM, hard disk or anyother information processing device with which the computer 600communicates, such as a server or computer.

Further, the aspects of the disclosure may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with CPU 601 and anoperating system such as Microsoft Windows 7, UNIX, Solaris, LINUX,Apple MAC-OS and other systems known to those skilled in the art.

The hardware elements of the computer 600 may be realized by variouscircuitry elements, known to those skilled in the art. For example, CPU601 may be a Xenon or Core processor from Intel of America or an Opteronprocessor from AMD of America, or may be other processor types thatwould be recognized by one of ordinary skill in the art. Alternatively,the CPU 601 may be implemented on an FPGA, ASIC, PLD or using discretelogic circuits, as one of ordinary skill in the art would recognize.Further, CPU 601 may be implemented as multiple processors cooperativelyworking in parallel to perform the instructions of the inventiveprocesses described above.

The computer 600 in FIG. 6 also includes a network controller 606, suchas an Intel Ethernet PRO network interface card from Intel Corporationof America, for interfacing with network 652. As can be appreciated, thenetwork 652 can be a public network, such as the Internet, or a privatenetwork such as an LAN or WAN network, or any combination thereof andcan also include PSTN or ISDN sub-networks. The network 652 can also bewired, such as an Ethernet network, or can be wireless such as acellular network including EDGE, 3G and 4G wireless cellular systems.The wireless network can also be WiFi, Bluetooth, or any other wirelessform of communication that is known. The computer 600 can communicate toone or more remote computers, such as a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node.

The computer 600 further includes a display controller 608, such as aNVIDIA GeForce GTX or Quadro graphics adaptor from NVIDIA Corporation ofAmerica for interfacing with display 610, such as a Hewlett PackardHPL2445w LCD monitor. A general purpose I/O interface 612 interfaceswith a keyboard and/or mouse 614 as well as a touch screen panel 616 onor separate from display 610. General purpose I/O interface alsoconnects to a variety of peripherals 618 including printers andscanners, such as an OfficeJet or DeskJet from Hewlett Packard.

A sound controller 620 is also provided in the computer 600, such asSound Blaster X-Fi Titanium from Creative, to interface withspeakers/microphone 622 thereby providing sounds and/or audio.

The general purpose storage controller 624 connects the storage mediumdisk 604 with communication bus 626, which may be an ISA, EISA, VESA,PCI, or similar, for interconnecting all of the components of thecomputer 600. A description of the general features and functionality ofthe display 610, keyboard and/or mouse 614, as well as the displaycontroller 608, storage controller 624, network controller 606, soundcontroller 620, and general purpose I/O interface 612 is omitted hereinfor brevity as these features are known.

The exemplary circuit elements described in the context of the presentdisclosure may be replaced with other elements and structureddifferently than the examples provided herein. Moreover, circuitryconfigured to perform features described herein may be implemented inmultiple circuit units (e.g., chips), or the features may be combined incircuitry on a single chip.

FIG. 7 shows an exemplary data processing system 700, according tocertain embodiments, for implementing some or all of the aspects of thedisclosure including the coding scheme, the reading processing system420, and the process 500 described above. The data processing system 700is an example of a computer in which specific code or instructionsimplementing the processes of the illustrative embodiments may belocated to create a particular machine for implementing the above-notedprocess.

In FIG. 7, data processing system 700 employs a hub architectureincluding a north bridge and memory controller hub (NB/MCH) 725 and asouth bridge and input/output (I/O) controller hub (SB/ICH) 720. Thedata processing system 700 includes a central processing unit (CPU) 730that is connected to NB/MCH 725. The CPU 730 may be part of thecircuitry for implementing the reading vector decoding device 423 ofFIG. 4 and performs the functions and processes described above. TheNB/MCH 725 also connects to the memory 745 via a memory bus, andconnects to the graphics processor 750 via an accelerated graphics port(AGP). The NB/MCH 725 also connects to the SB/ICH 720 via an internalbus (e.g., a unified media interface or a direct media interface). TheCPU 730 may contain one or more processors and even may be implementedusing one or more heterogeneous processor systems.

For example, FIG. 8 shows an exemplary implementation of CPU 730according to an embodiment of the disclosure. In one implementation, theinstruction register 838 retrieves instructions from the fast memory840. At least part of these instructions are fetched from theinstruction register 838 by the control logic 836 and interpretedaccording to the instruction set architecture of the CPU 730. Part ofthe instructions can also be directed to the register 832. In oneimplementation, the instructions are decoded according to a hardwiredmethod, and in another implementation, the instructions are decodedaccording a microprogram that translates instructions into sets of CPUconfiguration signals that are applied sequentially over multiple clockpulses. After fetching and decoding the instructions, the instructionsare executed using the arithmetic logic unit (ALU) 834 that loads valuesfrom the register 832 and performs logical and mathematical operationson the loaded values according to the instructions. The results fromthese operations can be feedback into the register and/or stored in thefast memory 840. According to certain implementations, the instructionset architecture of the CPU 730 can use a reduced instruction setarchitecture, a complex instruction set architecture, a vector processorarchitecture, a very large instruction word architecture. Furthermore,the CPU 730 can be based on the Von Neuman model or the Harvard model.The CPU 730 can be a digital signal processor, an FPGA, an ASIC, a PLA,a PLD, or a CPLD. Further, the CPU 730 can be an x86 processor by Intelor by AMD; an ARM processor, a Power architecture processor by, e.g.,IBM; a SPARC architecture processor by Sun Microsystems or by Oracle; orother known CPU architecture.

Referring again to FIG. 7, the data processing system 700 can includethat the SB/ICH 720 is coupled through a system bus to an I/O Bus, aread only memory (ROM) 756, universal serial, bus (USB) port 764, aflash binary input/output system (BIOS) 768, and a graphics controller758. PCI/PCIe devices can also be coupled to SB/ICH 720 through a PCIbus 762.

The PCI devices may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. The Hard disk drive 760 andCD-ROM 766 can use, for example, an integrated drive electronics (IDE)or serial advanced technology attachment (SATA) interface. In oneimplementation, the I/O bus can include a super I/O (SIO) device.

Further, the hard disk drive (HDD) 760 and optical drive 766 can also becoupled to the SB/ICH 720 through a system bus. In one implementation, akeyboard 770, a mouse 772, a parallel port 778, and a serial port 776can be connected to the system bus through the I/O bus. Otherperipherals and devices that can be connected to the SB/ICH 720 using amass storage controller such as SATA or PATA, an Ethernet port, an ISAbus, a LPC bridge, SMBus, a DMA controller, and an Audio Codec.

Moreover, the present disclosure is not limited to the specific circuitelements described herein, nor is the present disclosure limited to thespecific sizing and classification of these elements. For example, theskilled artisan will appreciate that the circuitry described herein maybe adapted based on changes on battery sizing and chemistry, or based onthe requirements of the intended back-up load to be powered.

The functions and features described herein may also be executed byvarious distributed components of a system. For example, one or moreprocessors may execute these system functions, wherein the processorsare distributed across multiple components communicating in a network.The distributed components may include one or more client and servermachines, which may share processing, in addition to various humaninterface and communication devices (e.g., display monitors, smartphones, tablets, personal digital assistants (PDAs)). The network may bea private network, such as a LAN or WAN, or may be a public network,such as the Internet. Input to the system may be received via directuser input and received remotely either in real-time or as a batchprocess. Additionally, some implementations may be performed on modulesor hardware not identical to those described. Accordingly, otherimplementations are within the scope that may be claimed.

The above-described hardware description is a non-limiting example ofcorresponding structure for performing the functionality describedherein.

The hardware description above, exemplified by any one of the structureexamples shown in FIG. 6, 7, or 8, constitutes or includes specializedcorresponding structure that is programmed or configured to perform thefunctions and processes described in FIGS. 2-5.

It is noted that although examples described above refer to Hamming codethat is capable of correcting an error in code words for datatransmission, the aspects of the disclosure are not limited tocorrecting an error of the reading vectors of the electricity meters.Other linear systematic block codes, such as Reed-Muller code capable ofdetecting and correcting multiple errors in code words for datatransmissions can be used for detecting and correcting multiple errorsin the reading vectors of the electricity meters.

While aspects of the present disclosure have been described inconjunction with the specific embodiments thereof that are proposed asexamples, alternatives, modifications, and variations to the examplesmay be made. Accordingly, embodiments as set forth herein are intendedto be illustrative and not limiting. There are changes that may be madewithout departing from the scope of the claims set forth below.

1: A method for correcting electricity meter readings, comprising:receiving first readings of regular meters measuring electric energydelivered in each of a group of cables fed from a same distribution nodedelivering electric energy to one or more electric energy transmissionsystems in an electric grid comprising multiple electric energytransmission systems during a period of time, wherein the regular metersare installed at consumers of the electric energy delivered by the groupof cables; receiving second readings of check meters measuring electricenergy delivered in each of combinations of cables in the group ofcables during the period of time, the combinations of cables beingformed based on a redundant matrix in a generator matrix of a linearsystematic block code, wherein the check meters are installed atelectric energy distribution nodes connected to the group of cables; andcorrecting, in response to determining that at least one error has beendetected, the at least one error in the first readings of the regularmeters and the second readings of the check meters based on a syndromevector of a reading vector that includes the first readings of theregular meters and the second readings of the check meters, the syndromevector of the reading vector being computed using a decoding matrix thatis obtained by modifying a parity-check matrix of the linear systematicblock code. 2: The method of claim 1, wherein the first readings of theregular meters correspond to parameters regarding usage of the electricenergy delivered in each of the group of cables during the period oftime, and the second readings of the check meters correspond toparameters regarding usage of the electric energy delivered in each ofcombinations of cables in the group of cables during the period of time.3: The method of claim 1, wherein the syndrome vector of the readingvector is computed by multiplying the reading vector with the decodingmatrix. 4: The method of claim 1, wherein modifying the parity-checkmatrix includes multiplying elements of an identity matrix in theparity-check matrix with −1. 5: The method of claim 1, wherein thedecoding matrix is a modified decoding matrix, anti modifying theparity-check matrix includes multiplying an element of an identitymatrix in the parity-check matrix with −1, and scaling an element of theredundant matrix in the parity-check matrix using an estimated fractionof power loss in one of the group of cables with respect to totalelectric energy delivered in the one cable. 6: The method of claim 5,further comprising: determining that no error has been detected whenvalues of elements of the syndrome vector are within a predeterminedrange, the predetermined range being determined based on accuracy ofestimation of the power losses on the group of cables. 7: The method ofclaim 6, wherein the correcting of the at least one error in the firstreadings of the regular meters and the second readings of the checkmeters based on the syndrome vector of a reading vector includescomparing the syndrome vector with rows in the decoding matrix todetermine a position of a reading with the at least one error in thereading vector, and subtracting a value of the at least one error fromthe reading with the at least one error, the value of the at least oneerror being approximated using a value of an element in the syndromevector that is not in the predetermined range. 8: The method of claim 1,wherein each of the combinations of cables corresponds to a column ofthe redundant matrix, each element in the column corresponding to one ofthe group of cables, and each of the combinations of cables includescables corresponding to non-zero elements in the column of the redundantmatrix. 9: The method of claim 1, wherein the linear systematic blockcode is one of the following codes: (1) Golay Code; (2) Reed-Mullercode; (3) Hamming code. 10-20. (canceled) 21: The method of claim 1,wherein the electric energy distribution nodes are transformers.